Results 51 to 60 of about 1,819,138 (329)

On Incorporating Prior Knowledge Extracted From Large Language Models Into Causal Discovery

open access: yesIEEE Access
Large Language Models (LLMs) can reason about causality by leveraging vast pre-trained knowledge and text descriptions of datasets, demonstrating their effectiveness even when data is scarce.
Chanhui Lee   +12 more
doaj   +1 more source

Causal Discovery Under a Confounder Blanket [PDF]

open access: yes, 2022
Inferring causal relationships from observational data is rarely straightforward, but the problem is especially difficult in high dimensions. For these applications, causal discovery algorithms typically require parametric restrictions or extreme ...
Silva, R, Watson, DS
core  

Selecting robust features for machine-learning applications using multidata causal discovery [PDF]

open access: yes, 2023
Robust feature selection is vital for creating reliable and interpretable machine-learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal ...
Sudheesh, Saranya Ganesh   +11 more
core   +1 more source

Symmetry-Aware Transformers for Asymmetric Causal Discovery in Financial Time Series

open access: yesSymmetry
Financial markets exhibit fundamental asymmetries in temporal causality, where policy interventions create asymmetric transmission patterns that traditional symmetric modeling approaches fail to capture. This work introduces a mathematical framework that
Wenxia Zheng, Wenhe Liu
semanticscholar   +1 more source

Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph

open access: yesIEEE Access
In causal learning, discovering the causal graph of the underlying generative mechanism from observed data is crucial. However, real-world data for causal discovery is scarce and expensive, leading researchers to rely on synthetic datasets, which may not
Tingpeng Li   +5 more
doaj   +1 more source

Serum Myonectin Levels Are Positively Associated With Physical Function and Lower Frailty‐Related Limitation in Maintenance Hemodialysis Patients: A Cross‐Sectional Study

open access: yesTherapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Maintenance hemodialysis (MHD) patients frequently suffer from frailty, characterized by reduced physical function and poor prognosis. Myokines, such as myonectin, secreted by muscle, are emerging regulators of systemic health. This study investigated the relationship between serum myonectin, adipokines (adiponectin, omentin), and ...
Kenichi Kono   +7 more
wiley   +1 more source

Mental health progress requires causal diagnostic nosology and scalable causal discovery

open access: yesFrontiers in Psychiatry, 2022
Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other ...
Glenn N. Saxe   +4 more
doaj   +1 more source

Causality, Causal Discovery, and Causal Inference in Structural Engineering

open access: yesCoRR, 2022
Much of our experiments are designed to uncover the cause(s) and effect(s) behind a data generating mechanism (i.e., phenomenon) we happen to be interested in. Uncovering such relationships allows us to identify the true working of a phenomenon and, most importantly, articulate a model that may enable us to further explore the phenomenon on hand and/or
openaire   +2 more sources

Confidence in Causal Discovery with Linear Causal Models

open access: yes, 2021
Structural causal models postulate noisy functional relations among a set of interacting variables. The causal structure underlying each such model is naturally represented by a directed graph whose edges indicate for each variable which other variables it causally depends upon. Under a number of different model assumptions, it has been shown that this
David Strieder   +3 more
openaire   +3 more sources

Stable Differentiable Causal Discovery

open access: yesCoRR, 2023
Inferring causal relationships as directed acyclic graphs (DAGs) is an important but challenging problem. Differentiable Causal Discovery (DCD) is a promising approach to this problem, framing the search as a continuous optimization. But existing DCD methods are numerically unstable, with poor performance beyond tens of variables.
Achille Nazaret   +3 more
openaire   +3 more sources

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